Found 45 repositories(showing 30)
gavisangavi2502-max
This project requires the implementation and rigorous evaluation of a sophisticated deep learning model, specifically an LSTM or Transformer network, for multivariate time series forecasting.
dharanidharan23032000-create
LSTM based stock price prediction for Reliance Industries using daily historical data. Includes feature engineering (SMA, EMA, returns, volatility), dual prediction (price + returns), 60-day future forecast, candlestick visualization and SHAP explainability for feature impact.
Time Series Forecasting using PyTorch LSTM with synthetic data generation, baseline comparison (ARIMA & ETS), and model explainability using SHAP.
No description available
sathyavarsha2011-art
No description available
This project implements a complete deep learning pipeline for forecasting complex, multivariate time-series data using a stacked LSTM neural network. The dataset is programmatically generated to simulate realistic non-stationary behavior with seasonality, trend, and noise—similar to energy consumption or financial market data.
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archanavisu02-pixel
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archanavisu02-pixel
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klenantons1492001-pixel
Advanced Time Series Forecasting with Deep Learning and Explainability
No description available
sathyavarsha2011-art
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sathyavarsha2011-art
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baskarasethupathy2481
This project centers on forecasting complex time series data using state-of-the-art deep learning models, specifically LSTM and Transformer architectures. You’ll work with real-world or simulated multi-variable datasets to predict future values while also applying explainability tools to reveal how the model makes its predictions.
No description available
Advanced Time Series Forecasting using LSTM compares deep learning with ARIMA for multivariate multi-step prediction. Synthetic data with trend, seasonality, and noise was generated. Models were evaluated using RMSE, MAE, and MAPE with expanding window validation. SHAP was used to interpret feature contributions and improve transparency.
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Manimaranarockiyadoss
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Advanced time-series forecasting uses deep learning models like LSTMs and Transformers to capture complex temporal patterns and long-range dependencies. Techniques such as attention, feature selection, and SHAP enable model interpretability, while multi-horizon prediction and uncertainty estimation improve accuracy and reliability.
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thepraveenkumar2001
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“This project builds an advanced multivariate time series forecasting system using LSTM and Transformer models. It predicts future demand from historical features and includes preprocessing, model training, evaluation, and SHAP-based explainability for feature importance.”
rajiveerasamybt-ops
No description available